Databricks vs OpenSearch
- ✦ Delta Lake (Open Table Format)
- ✦ Apache Spark
- ✦ MLflow (ML Tracking)
- ✦ Full-text search
- ✦ k-NN vector search
- ✦ SQL query support
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Databricks Delta Lake adds full ACID guarantees to Parquet files on S3 or ADLS, enabling concurrent reads and writes that corrupt data in plain Parquet pipelines without managing separate lock services.
Databricks integrates MLflow natively, auto-logging parameters, metrics, and model artifacts for every training run, reducing experiment comparison from hours of manual log parsing to a 30-second dashboard review.
Databricks Structured Streaming processes Kafka events with exactly-once semantics and checkpointed state, supporting stateful aggregations across time windows without losing events on job restart.
Migrate from Elasticsearch to OpenSearch with API-compatible clients and keep the same application code
Route CloudWatch logs to OpenSearch Service via Kinesis for centralized log search without leaving AWS
Store document embeddings and run hybrid text+vector search with the k-NN plugin to improve retrieval accuracy
Correlate AWS CloudTrail and VPC flow logs in OpenSearch to detect anomalous access patterns in near-real-time
Pricing Intelligence
Databricks Plans
Paid plans only
- • From $0.07/DBU
- • Delta Lake
- • Collaborative notebooks
- • RBAC
- • Audit logs
- • Delta Sharing
- • Full platform
- • Dedicated support
- • Custom contracts
OpenSearch Plans
Free tier available
- • Apache 2.0 license
- • Self-hosted
- • Full features
- • From $0.096/hr per instance
- • Managed by AWS
- • Pay-as-you-go
Feature Matrix
10 differences found across 18 standardized features
- •Delta Lake (Open Table Format)
- •Apache Spark
- •MLflow (ML Tracking)
- •Unity Catalog (Governance)
- •Databricks SQL
- •Notebooks
- •Delta Live Tables (ETL)
- •Feature Store
- •Model Serving
- •Vector Search
- •Mosaic AI (LLM Fine-tuning)
- •AutoML
- •Workflows (Orchestration)
- •Delta Sharing
- •Multi-Cloud
- •Lakehouse Architecture
- •Full-text search
- •k-NN vector search
- •SQL query support
- •Distributed sharding
- •ML inference nodes
- •Security plugin (built-in)
- •Observability dashboards
- •Anomaly detection
- •Index rollups
- •REST API
- •Cross-cluster replication
- •Snapshot to S3
- •ISM (Index State Management)
- •Alerting plugin
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +Lakehouse architecture eliminates ETL between data lake and warehouse
- +MLflow is the de-facto ML experiment tracking standard
- +Unity Catalog provides unified governance across data and AI
- +Delta Lake open format avoids vendor lock-in
- −Steep learning curve: significant investment to operationalize
- −DBU pricing can be difficult to forecast
- +Apache 2.0 license - no SSPL restrictions
- +API-compatible with Elasticsearch for easy migration
- +Built-in security plugin at no extra cost vs Elastic
- −Feature development lags Elasticsearch in some areas
- −AWS-managed version has less flexibility than self-managed Elastic
At a Glance
Frequently Asked Questions
Related Comparisons
Sources
- 1.Databricks Official Pricing–Vendor pricing page
- 2.OpenSearch Official Pricing–Vendor pricing page
- 3.Databricks Official Website–Official product website
- 4.OpenSearch Official Website–Official product website